Visualization

This sub package provides different helper functions for visualizing data using plots.

Examples

The following code cell will plot a classification report of true labels versus predicted labels.

from helperfns.visualization import plot_complicated_confusion_matrix, plot_images, plot_images_predictions, plot_simple_confusion_matrix,
plot_classification_report, plot_wordcloud

# plotting classification report
fig, ax = plot_classification_report(labels, preds,
                    title='Classification Report',
                    figsize=(10, 5), dpi=70,
                    target_names = classes)

The plot_classification_report takes the following arguments:

Argument	Description	Type
images	List of images to plot	list
labels_true	True labels	list
labels_pred	Predicted labels	list
classes	List of class labels (default: [])	list
cols	Number of columns in the plot (default: 5)	int
rows	Number of rows in the plot (default: 3)	int
fontsize	Font size for labels (default: 16)	int
save_path	The path to save a plotted figure (default:None)	str

The plot_images_predictions plots the image predictions. This functions is very useful when you are doing image classification.

# plot predicted image labels with the images
plot_images_predictions(images, true_labels, preds, classes=["dog", "cat"] ,cols=8)

Here is the table of arguments for the plot_images_predictions

Argument	Description	Type
images	List of images to plot	list
labels_true	True labels	list
labels_pred	Predicted labels	list
classes	List of class labels (default: [])	list
cols	Number of columns in the plot (default: 5)	int
rows	Number of rows in the plot (default: 3)	int
fontsize	Font size for labels (default: 16)	int
save_path	The path to save a plotted figure (default:None	str

The plot_images functions is used to visualize images.

# plot the images with their labels
plot_images(images[:24], true_labels[:24], cols=8)

The plot_images takes the following as arguments:

Argument	Description	Type
images	List of images to plot	list
labels	List of labels corresponding to images	list
cols	Number of columns in the plot (default: 5)	int
rows	Number of rows in the plot (default: 3)	int
fontsize	Font size for labels (default: 16)	int
save_path	The path to save a plotted figure (default:None	str

The plot_simple_confusion_matrix is used to plot a less more verbose confusion matrix of real labels against predicted labels.

# plot a simple confusion matrix
y_true = [random.randint(0, 1) for _ in range (100)]
y_pred = [random.randint(0, 1) for _ in range (100)]
classes =["dog", "cat"]
plot_simple_confusion_matrix(y_true, y_pred, classes)

This function takes in the following in the following as arguments.

Argument	Description	Type
y_true	True labels	list
y_pred	Predicted labels	list
classes	List of class labels (default: [])	list
figsize	Size of the figure (default: (10, 10))	tuple
fontsize	Font size for labels (default: 15)	int
save_path	The path to save a plotted figure (default:None	str

The plot_complicated_confusion_matrix is used to plot a more verbose confusion matrix of real labels against predicted labels.

# plot a confusion matrix with percentage value of confusion
y_true = [random.randint(0, 1) for _ in range (100)]
y_pred = [random.randint(0, 1) for _ in range (100)]
classes =["dog", "cat"]
plot_complicated_confusion_matrix(y_true, y_pred, classes)

This function takes in the following as arguments.

Argument	Description	Type
y_true	True labels	list
y_pred	Predicted labels	list
classes	List of class labels (default: [])	list
figsize	Size of the figure (default: (5, 5))	tuple
fontsize	Font size for labels (default: 20)	int
title	Title of the plot (default: “Confusion Matrix”)	str
xlabel	Label for x-axis (default: “Predicted label”)	str
ylabel	Label for y-axis (default: “True label”)	str
save_path	The path to save a plotted figure (default:None	str

The plot_wordcloud function generates and plots a word cloud based on the provided corpus.

# Generate a word cloud from a sample text
corpus = "This is a sample text for generating word clouds"
plot_wordcloud(corpus, max_words=500, mask="wine")

This function takes in the following as arguments.

Argument	Description	Type
corpus	The text or dictionary of word frequencies to generate the word cloud from.	str or dict
max_words	Maximum number of words to include in the word cloud, default is 1,000.	int
title	Title of the plot, default is “Word Cloud”.	str
mask	The shape mask for the word cloud. Options are “head”, “chicken”, “wine”, “apple”, “tree” or None, default is “tree”.	Union[Literal[“head”, “chicken”, “wine”, “apple”, “tree”], None]
background_color	The background color of the word cloud, default is “#E4E0E1”.	str
contour_width	Width of the contour around the word cloud, default is 1.	int
contour_color	Color of the contour around the word cloud, default is “#D6C0B3”.	str
figsize	The figure size of the word cloud plot, default is (10, 10).	tuple
fontsize	Font size for the plot title, default is 15.	int
save_path	The path to save the plotted figure (default: None).	str or None